Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "127" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 31 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.02% | 2.21% | 0.080446 | -0.502212 | -0.212668 | 0.106455 | 0.972039 | 0.246709 | 0.106820 | 1.964174 | 0.7576 | 0.7595 | 0.3609 | 0.865316 | 0.942191 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.453641 | 1.112768 | -0.750032 | -0.614165 | -0.220645 | 2.694474 | -1.006237 | 0.141540 | 0.0249 | 0.0243 | 0.0006 | nan | nan |
| 2459843 | digital_ok | 0.00% | 0.66% | 0.66% | 0.00% | 17.93% | 0.54% | -1.170152 | -0.643506 | -0.616989 | -0.093278 | -0.656884 | 0.460049 | -0.546594 | 2.236301 | 0.7595 | 0.7547 | 0.3843 | 1.039673 | 1.006711 |
| 2459842 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.242342 | -0.369926 | 0.343470 | 0.960664 | 0.044559 | 0.806241 | -0.029266 | 0.664036 | 0.7655 | 0.7142 | 0.2380 | 2.129754 | 2.045792 |
| 2459841 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.806743 | 0.311424 | -0.477584 | -0.438714 | 0.273075 | 2.125282 | -0.619319 | 0.510271 | 0.0249 | 0.0243 | 0.0007 | nan | nan |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.386357 | -0.450437 | -0.560368 | -0.945356 | -0.251669 | 1.103215 | -0.640509 | 0.187271 | 0.0236 | 0.0231 | 0.0007 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | 1.127231 | 1.667481 | -0.876594 | -0.618166 | 0.988529 | 1.060579 | -1.029099 | -0.303759 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 304.369440 | 303.788588 | inf | inf | 12454.792971 | 12514.518947 | 6151.689632 | 6191.386201 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0429 | 0.0469 | 0.0017 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.697412 | -1.350613 | 0.525053 | -0.233338 | -0.768499 | 1.538662 | -0.865765 | -0.337650 | 0.0416 | 0.0428 | 0.0019 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.475218 | 0.172193 | 0.682869 | 0.324134 | -0.565526 | 1.397006 | -0.224968 | 0.362476 | 0.0370 | 0.0367 | 0.0012 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.577828 | -0.261439 | 0.019299 | -0.431933 | 2.452208 | 0.521991 | -0.031978 | 0.890566 | 0.7468 | 0.4809 | 0.5530 | 1.686623 | 1.537752 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.680405 | 1.251758 | -0.952076 | -0.640846 | -0.099239 | 0.120546 | -0.675084 | -0.157117 | 0.0430 | 0.0292 | 0.0010 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.350146 | -0.016385 | 0.053014 | -0.308128 | 0.658332 | 1.516743 | 0.345259 | 1.614689 | 0.7427 | 0.4700 | 0.5533 | 1.881561 | 1.501057 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.835193 | -0.446342 | 0.092521 | -0.437171 | 0.251197 | 0.111736 | 5.258446 | 2.624562 | 0.6826 | 0.6025 | 0.4190 | 5.028418 | 5.119571 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.001563 | 0.087592 | -0.262080 | -0.317677 | 0.157869 | 0.837549 | 0.675669 | 1.288078 | 0.7420 | 0.4910 | 0.5301 | 1.952817 | 1.470940 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.351242 | -0.250753 | -0.024074 | -0.539725 | -0.080819 | 0.342605 | -0.140884 | -0.084790 | 0.6972 | 0.6192 | 0.4149 | 1.495813 | 1.307058 |
| 2459826 | digital_ok | 0.00% | 16.13% | 16.13% | 0.00% | 15.79% | 0.00% | -0.082620 | -0.310761 | 0.010189 | -0.528570 | 0.483186 | 1.066386 | 0.038470 | 1.092256 | 0.6623 | 0.4648 | 0.4451 | 1.531857 | 1.110363 |
| 2459825 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.186248 | -0.024230 | 0.359303 | -0.042400 | 1.928780 | 2.807952 | 1.239013 | 1.554438 | 0.0986 | 0.0828 | 0.0225 | 0.000000 | 0.000000 |
| 2459824 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.362152 | -0.593227 | 0.845350 | 0.173064 | 0.527219 | 0.517500 | 0.658851 | 0.690130 | 0.0995 | 0.0938 | 0.0255 | 0.000000 | 0.000000 |
| 2459823 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.205242 | -0.081809 | 0.196367 | -0.238519 | 1.332340 | 0.157754 | 0.572935 | 0.380172 | 0.0942 | 0.0830 | 0.0285 | 0.000000 | 0.000000 |
| 2459822 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.268611 | 0.322060 | 0.073263 | -0.222422 | 0.082957 | 0.430985 | -0.373287 | -0.831647 | 0.0954 | 0.0942 | 0.0242 | 0.000000 | 0.000000 |
| 2459821 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.093854 | -0.373765 | 0.094770 | -0.543466 | 0.480916 | 0.919419 | -0.687546 | 0.075811 | 0.0907 | 0.0862 | 0.0246 | 0.942334 | 0.959404 |
| 2459820 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.436694 | -0.239731 | 0.143339 | -0.223297 | -0.472340 | 0.508361 | -0.074930 | 1.123014 | 0.0894 | 0.0854 | 0.0211 | 0.000000 | 0.000000 |
| 2459817 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.083592 | -0.033517 | -0.052037 | -0.498924 | 0.512820 | -0.282016 | -0.075266 | -0.068941 | 0.0905 | 0.0806 | 0.0186 | 0.000000 | 0.000000 |
| 2459816 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.074158 | -0.048224 | 0.950517 | 0.481692 | 0.720419 | 0.975797 | 0.145351 | 0.496775 | 0.0921 | 0.0825 | 0.0187 | 1.401095 | 1.381536 |
| 2459815 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.060847 | 0.035110 | 0.628872 | -0.055618 | 0.639524 | 1.103584 | 0.089581 | 0.725200 | 0.0937 | 0.0920 | 0.0180 | 1.354813 | 1.336793 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.559001 | -0.495347 | -0.183225 | -0.734937 | 0.570686 | 0.573693 | 1.349612 | 1.449921 | 0.1390 | 0.1264 | 0.0369 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 1.964174 | -0.502212 | 0.080446 | 0.106455 | -0.212668 | 0.246709 | 0.972039 | 1.964174 | 0.106820 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 2.694474 | 0.453641 | 1.112768 | -0.750032 | -0.614165 | -0.220645 | 2.694474 | -1.006237 | 0.141540 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 2.236301 | -0.643506 | -1.170152 | -0.093278 | -0.616989 | 0.460049 | -0.656884 | 2.236301 | -0.546594 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Power | 0.960664 | -0.242342 | -0.369926 | 0.343470 | 0.960664 | 0.044559 | 0.806241 | -0.029266 | 0.664036 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 2.125282 | -0.806743 | 0.311424 | -0.477584 | -0.438714 | 0.273075 | 2.125282 | -0.619319 | 0.510271 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 1.103215 | -0.386357 | -0.450437 | -0.560368 | -0.945356 | -0.251669 | 1.103215 | -0.640509 | 0.187271 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Shape | 1.667481 | 1.667481 | 1.127231 | -0.618166 | -0.876594 | 1.060579 | 0.988529 | -0.303759 | -1.029099 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Power | inf | 303.788588 | 304.369440 | inf | inf | 12514.518947 | 12454.792971 | 6191.386201 | 6151.689632 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 1.538662 | -1.350613 | -0.697412 | -0.233338 | 0.525053 | 1.538662 | -0.768499 | -0.337650 | -0.865765 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 1.397006 | 0.172193 | 0.475218 | 0.324134 | 0.682869 | 1.397006 | -0.565526 | 0.362476 | -0.224968 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | ee Temporal Variability | 2.452208 | -0.577828 | -0.261439 | 0.019299 | -0.431933 | 2.452208 | 0.521991 | -0.031978 | 0.890566 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Shape | 1.251758 | 0.680405 | 1.251758 | -0.952076 | -0.640846 | -0.099239 | 0.120546 | -0.675084 | -0.157117 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 1.614689 | -0.350146 | -0.016385 | 0.053014 | -0.308128 | 0.658332 | 1.516743 | 0.345259 | 1.614689 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | ee Temporal Discontinuties | 5.258446 | -0.446342 | -0.835193 | -0.437171 | 0.092521 | 0.111736 | 0.251197 | 2.624562 | 5.258446 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 1.288078 | 0.087592 | -0.001563 | -0.317677 | -0.262080 | 0.837549 | 0.157869 | 1.288078 | 0.675669 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 0.342605 | -0.351242 | -0.250753 | -0.024074 | -0.539725 | -0.080819 | 0.342605 | -0.140884 | -0.084790 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 1.092256 | -0.310761 | -0.082620 | -0.528570 | 0.010189 | 1.066386 | 0.483186 | 1.092256 | 0.038470 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 2.807952 | -0.024230 | 0.186248 | -0.042400 | 0.359303 | 2.807952 | 1.928780 | 1.554438 | 1.239013 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | ee Power | 0.845350 | -0.362152 | -0.593227 | 0.845350 | 0.173064 | 0.527219 | 0.517500 | 0.658851 | 0.690130 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | ee Temporal Variability | 1.332340 | -0.081809 | 0.205242 | -0.238519 | 0.196367 | 0.157754 | 1.332340 | 0.380172 | 0.572935 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 0.430985 | 0.268611 | 0.322060 | 0.073263 | -0.222422 | 0.082957 | 0.430985 | -0.373287 | -0.831647 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 0.919419 | -0.373765 | 0.093854 | -0.543466 | 0.094770 | 0.919419 | 0.480916 | 0.075811 | -0.687546 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 1.123014 | -0.436694 | -0.239731 | 0.143339 | -0.223297 | -0.472340 | 0.508361 | -0.074930 | 1.123014 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | ee Temporal Variability | 0.512820 | -0.083592 | -0.033517 | -0.052037 | -0.498924 | 0.512820 | -0.282016 | -0.075266 | -0.068941 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 0.975797 | -0.048224 | -0.074158 | 0.481692 | 0.950517 | 0.975797 | 0.720419 | 0.496775 | 0.145351 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Variability | 1.103584 | 0.035110 | 0.060847 | -0.055618 | 0.628872 | 1.103584 | 0.639524 | 0.725200 | 0.089581 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 127 | N10 | digital_ok | nn Temporal Discontinuties | 1.449921 | -0.495347 | -0.559001 | -0.734937 | -0.183225 | 0.573693 | 0.570686 | 1.449921 | 1.349612 |